In the fields of computational modeling and high performance computing, modeling platforms are known which contain a modeling engine to receive a variety of modeling inputs, and then generate a precise modeled output based on those inputs. In conventional modeling platforms, the set of inputs are precisely known, and the function applied to the modeling inputs is precisely known, but the ultimate results produced by the modeling engine are not known until the input data is supplied and the modeling engine is run. For example, in an econometric modeling platform, inputs for a particular industry like housing can be fed into a modeling engine. Those inputs can include, for instance, prevailing finance rates, employment rates, average new-home costs, costs of building materials, rate of inflation, and other economic or other variables that can be fed into the modeling engine which is programmed or configured to accept those inputs, apply a function or other processing to those inputs, and generate an output such as projected new-home sales for a given period of time. Those results can then be used to analyze or forecast other details related to the subject industry, such as predicted sector profits or employment.
In many real-life analytic applications, however, the necessary inputs for a given subject or study may not be known, while, at the same time, a desired or target output may be known or estimated with some accuracy. For instance, the research and development (R&D) department of a given corporation may be fixed at the beginning of a year or other budget cycle, but the assignment or allocation of that available amount of funds to different research teams or product areas may not be specified by managers or others. In such a case, an analyst may have to manually estimate and “back out” distributions of budget funds to different departments to begin to work out a set of component funding amounts that will, when combined, produce the already-known overall R&D or other budget. In performing that interpolation, the analyst may or may not be in possession of some departmental component budgets which have themselves also been fixed, or may or may not be in possession of the computation function which will appropriately sum or combine all component funds to produce the overall predetermined target budget. Adjustment of one component amount by hand may cause or suggest changes in other components in a ripple effect, which the analyst will then have to examine or account for in a further iteration of the same manual estimates. In further regards, after generating or creating a set of combined input data including interpolated inputs that the user or analyst has deemed accurate or satisfactory, there may typically be no channel or mechanism by which the interpolated data object or objects can be directly accessed or retrieved by other application programs, such as spreadsheet or database applications. Moreover, the other downstream application programs that may wish to make use of the interpolated data object or objects may have no way to dynamically bind or link to that set of data, so that the results of later or further interpolation operations become available or ported to those separate applications, or results of local operations can be transmitted back to the interpolation engine, automatically.
It may be desirable to provide systems and methods for embedding an interpolated data object in an application data file, in which a user can access and/or generate interpolated data objects, and embed those object or objects in database, spreadsheet, and/or other local data files to permit operation on that data on a dynamically shared basis, in both upstream and downstream directions from the interpolated data source.